Exploring the Use of European Weather Regimes for Improving User-Relevant Hydrological Forecasts at the Subseasonal Scale in Switzerland

Across the globe, there has been an increasing interest in improving the predictability of subseasonal hydrometeorological forecasts, as they play a valuable role in medium- to long-term planning in many sectors, such as agriculture, navigation, hydropower, and emergency management. However, these forecasts still have very limited skill at the monthly time scale; hence, this study explores the possibilities for improving forecasts through different pre- and postprocessing techniques at the interface with a Precipitationn–Runoff–Evapotranspiration Hydrological Response Unit Model (PREVAH). Specifically, this research aims to assess the benefit of European weather regime (WR) data within a hybrid forecasting setup, a combination of a traditional hydrological model and a machine learning (ML) algorithm, to improve the performance of subseasonal hydrometeorological forecasts in Switzerland. The WR data contain information about the large-scale atmospheric circulation in the North Atlantic–European region, and thus allow the hydrological model to exploit potential flow-dependent predictability. Four hydrological variables are investigated: total runoff, baseflow, soil moisture, and snowmelt. The improvements in the forecasts achieved with the pre- and postprocessing techniques vary with catchments, lead times, and variables. Adding WR data has clear benefits, but these benefits are not consistent across the study area or among the variables. The usefulness of WR data is generally observed for longer lead times, e.g., beyond the third week. Furthermore, a multimodel approach is applied to determine the “best practice” for each catchment and improve forecast skill over the entire study area. This study highlights the potential and limitations of using WR information to improve subseasonal hydrometeorological forecasts in a hybrid forecasting system in an operational mode

Zu den Autorinnen und Autoren

Der vorliegende Band versammelt die umgearbeiteten Beiträge einer Tagung, die im März 2002 im Warburg-Haus in Hamburg stattfand. Sie wurde im Rahmen des Forschungsprojekts „Natur im Konflikt“ veranstaltet, das von der Volkswagenstiftung innerhalb des Förderprogramms „Schlüsselthemen der Geisteswissenschaften“ finanziert wird. Dieses interdisziplinäre Vorhaben widmet sich der Untersuchung von mentalen Konzepten, Bildern, Modellen und Wertzuschreibungen, die zum kollektiven Fundus unserer Vorstellungen von Natur gehören. Dabei richten sich die Untersuchungen aus der Perspektive verschiedener Fachrichtungen – Ethnologie bzw. Sozialanthropologie, Geschichtswissenschaft, naturwissenschaftliche Küstenforschung, Literatur-, Sprach- und Medienwissenschaft – insbesondere auf die diejenigen Naturbilder und Modellierungen, die zu den oft nicht thematisierten Argumentationen und Überzeugungen gehören.This volume collects the revised contributions of a conference that took place in March 2002 at the Warburg-Haus in Hamburg. It was organised as part of the "Nature in Conflict" research project, which was funded by the Volkswagen Foundation within the framework of the "Key Humanities Issues" funding programme. This interdisciplinary project was dedicated to the investigation of mental concepts, images, models and value attributions that belong to the collective fund of our ideas of nature. In this context, the investigations are directed from the perspective of various disciplines - ethnology or social anthropology, history, coastal research in the natural sciences, literature, linguistics and media studies - in particular at those images of nature and models that belong to the argumentations and convictions that are often not discussed

Multiscale Error Analysis, Correction and Predictive Uncertainty Estimation in a Flood Forecasting System

River discharge predictions often show errors that degrade the quality of forecasts. Three different methods of error correction are compared, namely, an autoregressive model with and without exogenous input (ARX and AR, respectively), and a method based on wavelet transforms. For the wavelet method, a Vector-Autoregressive model with exogenous input (VARX) is simultaneously fitted for the different levels of wavelet decomposition; after predicting the next time steps for each scale, a reconstruction formula is applied to transform the predictions in the wavelet domain back to the original time domain. The error correction methods are combined with the Hydrological Uncertainty Processor (HUP) in order to estimate the predictive conditional distribution. For three stations along the Danube catchment, and using output from the European Flood Alert System (EFAS), we demonstrate that the method based on wavelets outperforms simpler methods and uncorrected predictions with respect to mean absolute error, Nash-Sutcliffe efficiency coefficient (and its decomposed performance criteria), informativeness score, and in particular forecast reliability. The wavelet approach efficiently accounts for forecast errors with scale properties of unknown source and statistical structure.JRC.DDG.H.7-Land management and natural hazard

Post-Processing of Stream Flows in Switzerland with an Emphasis on Low Flows and Floods

Post-processing has received much attention during the last couple of years within the hydrological community, and many different methods have been developed and tested, especially in the field of flood forecasting. Apart from the different meanings of the phrase “post-processing” in meteorology and hydrology, in this paper, it is regarded as a method to correct model outputs (predictions) based on meteorological (1) observed input data, (2) deterministic forecasts (single time series) and (3) ensemble forecasts (multiple time series) and to derive predictive uncertainties. So far, the majority of the research has been related to floods, how to remove bias and improve the forecast accuracy and how to minimize dispersion errors. Given that global changes are driving climatic forces, there is an urgent need to improve the quality of low-flow predictions, as well, even in regions that are normally less prone to drought. For several catchments in Switzerland, different post-processing methods were tested with respect to low stream flow and flooding conditions. The complexity of the applied procedures ranged from simple AR processes to more complex methodologies combining wavelet transformations and Quantile Regression Neural Networks (QRNN) and included the derivation of predictive uncertainties. Furthermore, various verification methods were tested in order to quantify the possible improvements that could be gained by applying these post-processing procedures based on different stream flow conditions. Preliminary results indicate that there is no single best method, but with an increase of complexity, a significant improvement of the quality of the predictions can be achieved

Subseasonal hydrometeorological ensemble predictions in small- and medium-sized mountainous catchments: Benefits of the NWP approach

Traditional ensemble streamflow prediction (ESP) systems are known to provide a valuable baseline to predict streamflows at the subseasonal to seasonal timescale. They exploit a combination of initial conditions and past meteorological observations, and can often provide useful forecasts of the expected streamflow in the upcoming month. In recent years, numerical weather prediction (NWP) models for subseasonal to seasonal timescales have made large progress and can provide added value to such a traditional ESP approach. Before using such meteorological predictions two major problems need to be solved: the correction of biases, and downscaling to increase the spatial resolution. Various methods exist to overcome these problems, but the potential of using NWP information and the relative merit of the different statistical and modelling steps remain open. To address this question, we compare a traditional ESP system with a subseasonal hydrometeorological ensemble prediction system in three alpine catchments with varying hydroclimatic conditions and areas between 80 and 1700 km2. Uncorrected and corrected (pre-processed) temperature and precipitation reforecasts from the ECMWF subseasonal NWP model are used to run the hydrological simulations and the performance of the resulting streamflow predictions is assessed with commonly used verification scores characterizing different aspects of the forecasts (ensemble mean and spread). Our results indicate that the NWP-based approach can provide superior prediction to the ESP approach, especially at shorter lead times. In snow-dominated catchments the pre-processing of the meteorological input further improves the performance of the predictions. This is most pronounced in late winter and spring when snow melting occurs. Moreover, our results highlight the importance of snow-related processes for subseasonal streamflow predictions in mountainous regions.ISSN:1027-5606ISSN:1607-793

How different de-biasing techniques contribute to improve forecast quality and value for hydropower operations

ISSN:1029-7006ISSN:1607-796

Seamless forecasting of extreme events on a global scale

Early warning systems of extreme events, such as floods, droughts, strong winds and wild fires as well as vector-borne diseases, at the global scale, are essential due to the combined threat of increased population settlement in vulnerable areas and potential increase in the intensity of extreme weather due to climate change. The European Centre for Medium-Range Weather Forecasts (ECMWF) has in the last year developed prototype early warning systems for floods, droughts, extreme winds, wild forest fires and malaria transmission. This paper assesses the performance of these systems. By providing a comprehensive skill assessment both on a global level and in selected regions, we aim to assess their suitability for eventual integration into decision-support frameworks

Exploring the use of European weather regimes for improving user-relevant hydrological forecasts at the sub-seasonal scale in Switzerland

Across the globe, there has been an increasing interest in improving the predictability of sub-seasonal hydro-meteorological forecasts as they play a valuable role in medium- to long-term planning in many sectors such as agriculture, navigation, hydropower, and emergency management. However, these forecasts still have very limited skill at the monthly time scale; hence this study explores the possibilities for improving forecasts through different pre- and post-processing techniques at the interface with a hydrological model (PREVAH). Specifically, this research aims to assess the benefit from European Weather Regime (WR) data into a hybrid forecasting setup, a combination of a traditional hydrological model and a machine learning (ML) algorithm, to improve the performance of sub-seasonal hydro-meteorological forecasts in Switzerland. The WR data contains information about the large-scale atmospheric circulation in the North-Atlantic European region, and thus allows the hydrological model to exploit potential flow-dependent predictability. Four hydrological variables are investigated: total runoff, baseflow, soil moisture, and snowmelt. The improvements in the forecasts achieved with the pre- and post-processing techniques vary with catchments, lead times, and variables. Adding WR data has clear benefits, but these benefits are not consistent across the study area or among the variables. The usefulness of WR data is generally observed for longer lead times, e.g., beyond the third week. Furthermore, a multi-model approach is applied to determine the “best practice” for each catchment and improve forecast skill over the entire study area. This study highlights the potential and limitations of using WR information to improve sub-seasonal hydro-meteorological forecasts in a hybrid forecasting system in an operational mode.ISSN:1525-755XISSN:1525-754

State of the Art of Flood Forecasting - from Deterministic to Probabilistic Approaches

Flood forecasting systems form a key part of ¿preparedness¿ strategies for disastrous floods and provide hydrological services, civil protection authorities and the public with information of upcoming events. Provided the warning leadtime is sufficiently long, adequate preparatory actions can be taken to efficiently reduce the impacts of the flooding (Penning-Rowsell et al., 2000, de Roo et al., 2003). Already in 1674, Pierre Perrault established a quantitative relationship between rainfall and flow for the river Seine (Perrault, 1674) effectively allowing realtime forecasting, but only the development of technology, computers and numerical models starting in the 60ies made quantitative flood forecasts possible as we know it today. Increasingly powerful computing systems, data storage capacities and remote sensing technology has led to enhanced observational data collection systems, high resolution spatial data sets over land surfaces and the oceans, and complex mathematical models furthering the understanding of the complex physical hydro-meteorological processes in a river basin. Research has shown that the combination of the particular rainfall climatology in space and time and the manifold and interactive processes at the surface and in the soil result in such highly non-linear hydrologic responses that these become characteristic to this catchment only (Arnaud et al., 2002; Smith et al., 2004, Obled et al., 1994; Segond, 2006; Smith et al., 2004). Therefore, the design of the best flood forecasting system may differ from catchment to catchment. Such a system needs to balance the availability and quality of data on the one hand and the computational representation of the processes in the atmosphere, surface, soil and channels contributing to flooding on the other hand. Furthermore, it needs to respect the particular demands of the enduser, since decision makers have different priorities. For example, urban areas require a significantly different management approach than reservoir operations. Despite the differences in concept and data needs, there is one underlying issue that spans across all systems. There has been an increasing awareness and acceptance that uncertainty is a fundamental issue of flood forecasting and needs to be dealt with at the different spatial and temporal scales as well as at different stages of the flood generating processes (Cloke et al., 2009). The main sources of uncertainties arise either from input data (i.e., physical measurement errors, the difference in spatio-temporal scale between model and measurements, and meteorological forecasts) or from the model itself through the mathematical simplification and parameterisation of the different physical processes contributing to runoff (Thielen et al., 2008).JRC.H.7-Land management and natural hazard